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serving-llms-vllm

Deploy high-throughput LLM serving with OpenAI-compatible endpoints using vllm.

1|Updated May 21, 2026
Search Tags:#OpenAI API#inference optimization#tensor parallelism#LLM serving#GPU memory utilization
One-click install
npx skills add https://github.com/blueskies1818/hermesALIone --skill serving-llms-vllm-blueskies1818
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: serving-llms-vllm
Source: https://github.com/blueskies1818/hermesALIone/tree/main/Agent/skills/mlops/inference/vllm
Command: npx skills add https://github.com/blueskies1818/hermesALIone --skill serving-llms-vllm-blueskies1818

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires vllm, torch, transformers, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a high-throughput LLM serving solution for production environments, optimizing inference latency/throughput and enabling serving of models with limited GPU memory.

Core Features & Use Cases

  • High Throughput: Achieves 24x higher throughput than standard transformers through PagedAttention and continuous batching.
  • OpenAI API: Supports OpenAI-compatible endpoints.
  • Quantization: Enables quantization (GPTQ/AWQ/FP8) to fit large models in limited GPU memory.
  • Tensor Parallelism: Utilizes tensor parallelism for 30B-70B models with GPU memory utilization optimization.
  • Use Case: Use this Skill to deploy production-grade LLM APIs, optimize inference performance, and serve models with limited GPU resources.

Quick Start

Use the serving-llms-vllm skill to deploy a high-performance LLM server.

Frequently Asked Questions about serving-llms-vllm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize LLM serving throughput for production APIs?▼

Optimize LLM serving throughput for production APIs by using PagedAttention and continuous batching to achieve 24x higher throughput than standard transformers while maintaining OpenAI-compatible endpoints.

Can I serve 30B-70B LLMs with limited GPU memory?▼

You can serve 30B-70B LLMs with limited GPU memory by applying tensor parallelism and quantization techniques like GPTQ, AWQ, or FP8 to optimize GPU memory utilization and fit large models into available hardware.

Does this LLM serving approach support OpenAI API endpoints?▼

This LLM serving approach supports OpenAI-compatible API endpoints, allowing you to integrate the deployed high-throughput server directly into existing application pipelines that expect the OpenAI API format.

What is the best way to reduce inference latency for production LLM APIs?▼

Reduce inference latency for production LLM APIs by deploying an optimized serving runtime that implements continuous batching and PagedAttention, significantly lowering inference latency and increasing throughput compared to standard transformers.

Do I need specific dependencies to deploy a high-throughput LLM server?▼

Deploying a high-throughput LLM server requires specific dependencies including vllm, torch, and transformers, along with specific configuration for your GPU and model size to function properly.

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